The concept of Cloud-based services is a virtualization of resources—networks, servers, applications, data storage, and services running on the platform, which provide on-demand access to users. Cloud-based services including, Infrastructure-as-a-Service, Platform-as-a-Service, and Software-as-a-Service, have emerged as principal components of business processes in recent times. While cloud-based services make the use of resources more efficient it places a severe energy burden on the provider. Data centers in the cloud environment consume 1.5-2% of all global electricity, almost half of which is attributed to cooling them; data center energy consumption is growing at a rate of 12% a year. However, energy consumption to run cloud services not only includes energy demand at the underlying infrastructure layer and platform layer but also includes energy requirements pertaining to a service offered at the service layers.
More specifically, the infrastructure-specific energy demand is determined by the energy required to keep computing equipment, such as servers, racks, and network switches active and available to host the services. Each computing equipment has its own power profile. Further, the energy required by cooling units to maintain operating conditions contributes to the infrastructure-specific energy demand.
The platform-specific energy demand is determined by the system software running as a part of middleware and virtualization layer over the underlying infrastructure. Moreover, the infrastructure-specific energy demand and platform-specific energy demand depend on QoS (Quality of Service) parameters including, but not limited to, response time and throughput of the services.
As mentioned above, the service-specific energy demand contributes to the overall cloud energy consumption. Each service running on the platform incurs service-specific energy consumption. The service-specific energy demand depends on the type and duration of resources used by the services. Thus, the cloud hosting all of these services incurs high energy demands and operational costs. Given the high amount of energy consumption, efficiency trends have become imperative and consequently, companies are making efforts to reduce energy consumption.
There exist solutions in the market for energy management in the cloud environment but the existing studies have focused only on the platform and infrastructure layers. Of those, one such solution focuses on energy management at the equipment or infrastructure level—which typically includes allocation of servers in a data center, for example. This also includes platform power management, workload management, network switch management and cooling management. Energy management of cloud-infrastructure is therefore a well-studied area, both in academia and industry e.g., by IBM, HP, and VMware, however, they traditionally focused on energy-efficient provisioning and management of services at the infrastructure layer. Such approaches optimize the computing and cooling resource usage within and across data centers to: (i) reduce energy consumption while meeting performance demands, (ii) improve performance while meeting energy budgets, and (iii) reduce cost through control of energy consumption and performance parameters (e.g. throughput and delay). But these approaches fail to consider the energy metering at the service or software level.
In the past some green operations have focused on a “users-in-loop” approach. For example, SharePrint in the field of document printing services promotes the sharing of printed material among users by providing some kind of incentives for not printing the document. Such conventional users-in loop approaches focus on simple configuration with yes/no options, however, an effective solution requires more comprehensive configuration at the service layer because of multi-dimensional dependencies of configuration parameters on the green operation.
Another approach focuses on providing print job options to users such that paper usage can be optimized.
Existing cloud services offer resources to users without requiring the user to have knowledge of the servers that deliver such services. For instance, when the services are hosted on a cloud, the solutions provide very little or no knowledge to the user of how the choice of one or more services affects the overall energy consumption. The lack of transparency may lead to user dis-satisfaction. Thus, there is a need to provide a user-friendly solution allowing users to select the level of “green” as a trade-off of service quality.